Graduate and Postdoctoral Studies
Electrical and Computer Engineering
Model-Based Acquisition for Compressive Sensing & Imaging
Tuesday, April 9, 2013
to 12:00 PM
A227 Anderson Biological Laboratories
Compressive Sensing (CS) is a novel imaging technology based on the inherent redundancy of natural scenes. CS's applications in imaging or classification embodied in the Rice single-pixel camera are limited by its acquisition time. In this study, hybrid-subspace sparse sampling and secant projection on manifold data are applied to speed up imaging and classification tasks. Both methods considerably decrease the number of required measurements compared with the pure random patterns, and make CS applicable in the situation where quick acquisition is required.